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Genetic Algorithms: Principles and Perspectives

A Guide to GA Theory

  • Book
  • © 2002

Overview

Authors:
  1. Colin R. Reeves

    1. School of Mathematical and Information Sciences, Coventry University, UK

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    You can also search for this author in PubMed   Google Scholar

  2. Jonathan E. Rowe

    1. School of Computer Science, University of Birmingham, USA

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    You can also search for this author in PubMed   Google Scholar

  • Is a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops

Part of the book series: Operations Research/Computer Science Interfaces Series (ORCS, volume 20)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-x
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  2. Introduction

    Pages 1-18

  3. Basic Principles

    Pages 19-63

  4. Schema Theory

    Pages 65-93

  5. No Free Lunch for GAs

    Pages 95-109

  6. GAs as Markov Processes

    Pages 111-140

  7. The Dynamical Systems Model

    Pages 141-171

  8. Statistical Mechanics Approximations

    Pages 173-200

  9. Predicting GA Performance

    Pages 201-230

  10. Landscapes

    Pages 231-263

  11. Summary

    Pages 265-285

  12. Back Matter

    Pages 287-332
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Keywords

About this book

Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory is a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops. However, this theoretical work is still rather fragmented, and the authors believe that it is the right time to provide the field with a systematic presentation of the current state of theory in the form of a set of theoretical perspectives. The authors do this in the interest of providing students and researchers with a balanced foundational survey of some recent research on GAs. The scope of the book includes chapter-length discussions of Basic Principles, Schema Theory, "No Free Lunch", GAs and Markov Processes, Dynamical Systems Model, Statistical Mechanics Approximations, Predicting GA Performance, Landscapes and Test Problems.

Authors and Affiliations

  • School of Mathematical and Information Sciences, Coventry University, UK

    Colin R. Reeves

  • School of Computer Science, University of Birmingham, USA

    Jonathan E. Rowe

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